Many conventional approaches to detecting objects in images are based on a sliding window approach. For example, if the object to be detected is a face of an individual, the sliding window approach calls for sliding a rectangle across the image and collecting “face likelihood” information to implement face detection. One of various conventional techniques may be applied to implement the object detection, such as, for example, the Viola-Jones detection framework using the Haar-wavelet technique. These conventional techniques involve training a binary classifier from an image database that consists of both objects and non-objects and then sliding a rectangle across each image in the database to perform the detection and in some instances, varying the scale of the images based on the test image to be detected. Each subimage corresponding to the sliding rectangle is examined by the binary classifier to determine if it is an instance of the object category, e.g., a face.
However, existing use of the sliding window approach can provide poor results in certain circumstances. For example, for face detection, a test image may be a profile view of a face, a slanted view of a face, and/or may otherwise depict an occluded view of a face. The sliding window approach is limited with respect to detecting these types of faces because of the difficulty of learning a good classifier encoding all possible variations. Additionally, the sliding window approach can provide poor or no results for recognizing certain attributes on the test image.